Summary
Big-data management is currently placing a high demand on both the hardware performance level, e.g. access latency, storage capacity, cost performance, and on the cognitive level, e.g. data processing, architectures, and algorithms. The ever-growing pressure for big data creates urgent global challenges like energy consumption and memory efficiency. New device architectures beyond the von Neumann paradigm are demanded which are inspired by the biological synaptic operativity towards the so-called neuromorphic computational scheme. The memristor is the more viable device emulating the synaptic behavior. Advanced materials are required to make memristor-based circuits energetically sustainable and outperforming. The integration of 2D semiconductors in memristors is a promising path to tackle these global challenges within the neuromorphic computation. In this scenario, here we propose a single-element memristor cell design based on tellurene (2D tellurium) mimicking artificial synaptic behavior and thus enabling memristor applications. Tellurene offers solid advantages over other 2D players owing to the structural and chemical simplicity, the low thermal budget of its synthesis, and the versatility to adapt to rigid and flexible layouts. Our goals are the scalable production of a tellurene standard and its integration in single memristor cells and in cross-bar arrays of memristors aiming at the fabrication of a neuromorphic circuits. The peculiar production process makes tellurene fit to delamination and transfer to any kind of surface, either rigid or flexible, flat or curve, and readily available for testing in edge-computing applications like environmental sensing signal elaboration. The translational purpose is to further up technology transfer of the developed products (either materials and processes) by retrieving stake-holders among small- and medium enterprises addressing the AI computing market.
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| Web resources: | https://cordis.europa.eu/project/id/101187967 |
| Start date: | 01-10-2024 |
| End date: | 31-03-2026 |
| Total budget - Public funding: | - 150 000,00 Euro |
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Original description
Big-data management is currently placing a high demand on both the hardware performance level, e.g. access latency, storage capacity, cost performance, and on the cognitive level, e.g. data processing, architectures, and algorithms. The ever-growing pressure for big data creates urgent global challenges like energy consumption and memory efficiency. New device architectures beyond the von Neumann paradigm are demanded which are inspired by the biological synaptic operativity towards the so-called neuromorphic computational scheme. The memristor is the more viable device emulating the synaptic behavior. Advanced materials are required to make memristor-based circuits energetically sustainable and outperforming. The integration of 2D semiconductors in memristors is a promising path to tackle these global challenges within the neuromorphic computation. In this scenario, here we propose a single-element memristor cell design based on tellurene (2D tellurium) mimicking artificial synaptic behavior and thus enabling memristor applications. Tellurene offers solid advantages over other 2D players owing to the structural and chemical simplicity, the low thermal budget of its synthesis, and the versatility to adapt to rigid and flexible layouts. Our goals are the scalable production of a tellurene standard and its integration in single memristor cells and in cross-bar arrays of memristors aiming at the fabrication of a neuromorphic circuits. The peculiar production process makes tellurene fit to delamination and transfer to any kind of surface, either rigid or flexible, flat or curve, and readily available for testing in edge-computing applications like environmental sensing signal elaboration. The translational purpose is to further up technology transfer of the developed products (either materials and processes) by retrieving stake-holders among small- and medium enterprises addressing the AI computing market.Status
SIGNEDCall topic
ERC-2024-POCUpdate Date
24-11-2024
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